Philippa Kaur

1.Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Nye, J. A., Link, J. S., Hare, J. A. & Overholtz, W. J. Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Mar. Ecol. Prog. Ser. 393, 111–129 (2009).ADS 
Article 

Google Scholar 
3.Fodrie, F. J., Heck, K. L. Jr., Powers, S. P., Graham, W. M. & Robinson, K. L. Climate-related, decadal-scale assemblage changes of seagrass-associated fishes in the northern Gulf of Mexico. Glob. Change Biol. 16, 48–59 (2010).ADS 
Article 

Google Scholar 
4.Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Simpson, S. D. et al. Continental shelf-wide response of a fish assemblage to rapid warming of the sea. Curr. Biol. 21, 1565–1570 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355 (2017).8.Poloczanska, E. S. et al. Responses of marine organisms to climate change across oceans. Front. Mar. Sci. 3, 62 (2016).Article 

Google Scholar 
9.Oswald, S. A. & Arnold, J. M. Direct impacts of climatic warming on heat stress in endothermic species: seabirds as bioindicators of changing thermoregulatory constraints. Integr. Zool. 7, 121–136 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Boyles, J. G., Seebacher, F., Smit, B. & McKechnie, A. E. Adaptive thermoregulation in endotherms may alter responses to climate change. Integr. Comp. Biol. 51, 676–690 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Khaliq, I., Hof, C., Prinzinger, R., Böhning-Gaese, K. & Pfenninger, M. Global variation in thermal tolerances and vulnerability of endotherms to climate change. Proc. R. Soc. B Biol. Sci. 281, 20141097 (2014).Article 

Google Scholar 
12.Gibson-Reinemer, D. K., Sheldon, K. S. & Rahel, F. J. Climate change creates rapid species turnover in montane communities. Ecol. Evol. 5, 2340–2347 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Pörtner, H.-O. et al. Climate induced temperature effects on growth performance, fecundity and recruitment in marine fish: developing a hypothesis for cause and effect relationships in Atlantic cod (Gadus morhua) and common eelpout (Zoarces viviparus). Cont. Shelf Res. 21, 1975–1997 (2001).ADS 
Article 

Google Scholar 
14.Neuheimer, A., Thresher, R., Lyle, J. & Semmens, J. Tolerance limit for fish growth exceeded by warming waters. Nat. Clim. Chang. 1, 110–113 (2011).ADS 
Article 

Google Scholar 
15.Pörtner, H.-O. Climate variations and the physiological basis of temperature dependent biogeography: systemic to molecular hierarchy of thermal tolerance in animals. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 132, 739–761 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Buckley, L. B., Hurlbert, A. H. & Jetz, W. Broad-scale ecological implications of ectothermy and endothermy in changing environments. Glob. Ecol. Biogeogr. 21, 873–885 (2012).Article 

Google Scholar 
17.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Przeslawski, R., Falkner, I., Ashcroft, M. B. & Hutchings, P. Using rigorous selection criteria to investigate marine range shifts. Estuar. Coast. Shelf Sci. 113, 205–212 (2012).ADS 
Article 

Google Scholar 
20.Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18, 944–953 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
21.MacLeod, C. D. Global climate change, range changes and potential implications for the conservation of marine cetaceans: a review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

Google Scholar 
22.Sydeman, W., Poloczanska, E., Reed, T. & Thompson, S. Climate change and marine vertebrates. Science 350, 772–777 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Bowen, W. Role of marine mammals in aquatic ecosystems. Mar. Ecol. Prog. Ser. 158, 74 (1997).Article 

Google Scholar 
24.Williams, T. M., Estes, J. A., Doak, D. F. & Springer, A. M. Killer appetites: assessing the role of predators in ecological communities. Ecology 85, 3373–3384 (2004).Article 

Google Scholar 
25.Roman, J. et al. Whales as marine ecosystem engineers. Front. Ecol. Environ. 12, 377–385 (2014).Article 

Google Scholar 
26.Neutel, A.-M., Heesterbeek, J. A. & de Ruiter, P. C. Stability in real food webs: weak links in long loops. Science 296, 1120–1123 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Bascompte, J., Melián, C. J. & Sala, E. Interaction strength combinations and the overfishing of a marine food web. Proc. Natl. Acad. Sci. U.S.A. 102, 5443–5447 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Macnab, B. K. The Physiological Ecology of Vertebrates: A View from Energetics (Cornell University Press, 2002).
Google Scholar 
29.Robinson, R. A. et al. Climate change and migratory species (2005).30.Worthy, G. A. & Edwards, E. F. Morphometric and biochemical factors affecting heat loss in a small temperate cetacean (Phocoena phocoena) and a small tropical cetacean (Stenella attenuata). Physiol. Zool., 432–442 (1990).31.Koopman, H. N. Phylogenetic, ecological, and ontogenetic factors influencing the biochemical structure of the blubber of odontocetes. Mar. Biol. 151, 277–291 (2007).Article 

Google Scholar 
32.Adamczak, S. K., Pabst, D. A., McLellan, W. A. & Thorne, L. H. Do bigger bodies require bigger radiators? Insights into thermal ecology from closely related marine mammal species and implications for ecogeographic rules. J. Biogeogr. 47, 1193–1206 (2020).Article 

Google Scholar 
33.Silber, G. K. et al. Projecting marine mammal distribution in a changing climate. Front. Mar. Sci. 4, 413 (2017).Article 

Google Scholar 
34.Kaschner, K., Tittensor, D. P., Ready, J., Gerrodette, T. & Worm, B. Current and future patterns of global marine mammal biodiversity. PLoS ONE 6, e19653 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Salvadeo, C. J., Lluch-Belda, D., Gómez-Gallardo, A., Urbán-Ramírez, J. & MacLeod, C. D. Climate change and a poleward shift in the distribution of the Pacific white-sided dolphin in the northeastern Pacific. Endanger. Species Res. 11, 13–19 (2010).Article 

Google Scholar 
36.Kovacs, K. M., Lydersen, C., Overland, J. E. & Moore, S. E. Impacts of changing sea-ice conditions on Arctic marine mammals. Mar. Biodivers. 41, 181–194 (2011).Article 

Google Scholar 
37.MacLeod, C. D. et al. Climate change and the cetacean community of north-west Scotland. Biol. Cons. 124, 477–483 (2005).Article 

Google Scholar 
38.Higdon, J. W. & Ferguson, S. H. Loss of Arctic sea ice causing punctuated change in sightings of killer whales (Orcinus orca) over the past century. Ecol. Appl. 19, 1365–1375 (2009).PubMed 
Article 

Google Scholar 
39.Evans, P. G. & Hammond, P. S. Monitoring cetaceans in European waters. Mammal Rev. 34, 131–156 (2004).Article 

Google Scholar 
40.Kaschner, K., Quick, N. J., Jewell, R., Williams, R. & Harris, C. M. Global coverage of cetacean line-transect surveys: status quo, data gaps and future challenges. PLoS ONE 7, e44075 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Taylor, B. L., Martinez, M., Gerrodette, T., Barlow, J. & Hrovat, Y. N. Lessons from monitoring trends in abundance of marine mammals. Mar. Mamm. Sci. 23, 157–175 (2007).Article 

Google Scholar 
42.Pyenson, N. D. The high fidelity of the cetacean stranding record: insights into measuring diversity by integrating taphonomy and macroecology. Proc. R. Soc. B Biol. Sci. 278, 3608–3616 (2011).Article 

Google Scholar 
43.Leeney, R. H. et al. Spatio-temporal analysis of cetacean strandings and bycatch in a UK Wsheries hotspot. Biodivers. Conserv. 17, 2323–2338 (2008).Article 

Google Scholar 
44.Lambert, E. et al. Quantifying likely cetacean range shifts in response to global climatic change: implications for conservation strategies in a changing world. Endanger. Species Res. 15, 205–222 (2011).Article 

Google Scholar 
45.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Chang. 3, 919–925 (2013).ADS 
Article 

Google Scholar 
46.Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Nawojchik, R., St. Aubin, D. J. & Johnson, A. Movements and dive behavior of two stranded, rehabilitated long-finned pilot whales (Globicephala melas) in the northwest Atlantic. Mar. Mammal Sci. 19, 232–239 (2003).Article 

Google Scholar 
48.Bloch, D. et al. Short-term movements of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 9, 47–8 (2003).Article 

Google Scholar 
49.Hayes, S., Josephson, E., Maze‐Foley, K. & Rosel, P. US Atlantic and Gulf of Mexico marine mammal stock assessments–2019. NOAA Tech Memo NMFS‐NE 264 (2020).50.Gannon, D., Read, A., Craddock, J., Fristrup, K. & Nicolas, J. Feeding ecology of long-finned pilot whales Globicephala melas in the western North Atlantic. Mar. Ecol. Prog. Ser. Oldendorf 148, 1–10 (1997).ADS 
Article 

Google Scholar 
51.Harden Jones, F. R. In Animal migration. Soc. Exp. Biol. Sem. Ser. 13 (ed. Aidley, D. J.) 139–165 (Cambridge Univ. Press, 1981).
Google Scholar 
52.Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
53.Heide-Jørgensen, M. P. et al. Diving behaviour of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 8, 307–313 (2002).Article 

Google Scholar 
54.Baird, R. W., Borsani, J. F., Hanson, M. B. & Tyack, P. L. Diving and night-time behavior of long-finned pilot whales in the Ligurian Sea. Mar. Ecol. Prog. Ser. 237, 301–305 (2002).ADS 
Article 

Google Scholar 
55.Adamczak, S. K., McLellan, W. A., Read, A. J., Wolfe, C. L. & Thorne, L. H. The impact of temperature at depth on estimates of thermal habitat for short‐finned pilot whales. Mar. Mammal Sci. (2020).56.Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evolut. 4, 109–114 (2020).Article 

Google Scholar 
57.Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Chang. 9, 959–963 (2019).ADS 
Article 

Google Scholar 
58.Kleisner, K. M. et al. The effects of sub-regional climate velocity on the distribution and spatial extent of marine species assemblages. PLoS ONE 11, e0149220 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Kleisner, K. M. et al. Marine species distribution shifts on the US Northeast Continental Shelf under continued ocean warming. Prog. Oceanogr. 153, 24–36 (2017).ADS 
Article 

Google Scholar 
60.Kavanaugh, M. T., Rheuban, J. E., Luis, K. M. & Doney, S. C. Thirty-three years of ocean benthic warming along the US northeast continental shelf and slope: Patterns, drivers, and ecological consequences. J. Geophys. Res. Oceans 122, 9399–9414 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Grady, J. M. et al. Metabolic asymmetry and the global diversity of marine predators. Science 363 (2019).62.Williams, T. M. et al. The diving physiology of bottlenose dolphins (Tursiops truncatus). III. Thermoregulation at depth. J. Exp. Biol. 202, 2763–2769 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Pabst, D. A., Rommel, S. A. & McLELLAN, W. A. The emergence of whales 379–397 (Springer, 1998).Book 

Google Scholar 
64.McNab, B. K. Short-term energy conservation in endotherms in relation to body mass, habits, and environment. J. Therm. Biol 27, 459–466 (2002).Article 

Google Scholar 
65.Yeates, L. C. & Houser, D. S. Thermal tolerance in bottlenose dolphins (Tursiops truncatus). J. Exp. Biol. 211, 3249–3257 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans (2016).67.Kenney, R. D., Scott, G. P., Thompson, T. J. & Winn, H. E. Estimates of prey consumption and trophic impacts of cetaceans in the USA northeast continental shelf ecosystem. J. Northwest Atl. Fish. Sci. 22, 155–171 (1997).Article 

Google Scholar 
68.Read, A. J. & Brownstein, C. R. Considering other consumers: fisheries, predators, and Atlantic herring in the Gulf of Maine. Conserv. Ecol. 7, 2 (2003).
Google Scholar 
69.Overholtz, W. & Link, J. Consumption impacts by marine mammals, fish, and seabirds on the Gulf of Maine-Georges Bank Atlantic herring (Clupea harengus) complex during the years 1977–2002. ICES J. Mar. Sci. J. Conseil 64, 83–96 (2007).Article 

Google Scholar 
70.Smith, L. A., Link, J. S., Cadrin, S. X. & Palka, D. L. Consumption by marine mammals on the Northeast US continental shelf. Ecol. Appl. 25, 373–389 (2015).PubMed 
Article 

Google Scholar 
71.Estes, J. A., Tinker, M. T., Williams, T. M. & Doak, D. F. Killer whale predation on sea otters linking oceanic and nearshore ecosystems. Science 282, 473–476 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
72.Pace, M. L., Cole, J. J., Carpenter, S. R. & Kitchell, J. F. Trophic cascades revealed in diverse ecosystems. Trends Ecol. Evol. 14, 483–488 (1999).CAS 
PubMed 
Article 

Google Scholar 
73.Myers, R. A., Baum, J. K., Shepherd, T. D., Powers, S. P. & Peterson, C. H. Cascading effects of the loss of apex predatory sharks from a coastal ocean. Science 315, 1846–1850 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
74.Durant, J. M., Hjermann, D. Ø., Ottersen, G. & Stenseth, N. C. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. (CR) 33, 271–283 (2007).ADS 
Article 

Google Scholar 
75.Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479 (2008).PubMed 
Article 

Google Scholar 
76.Both, C., Van Asch, M., Bijlsma, R. G., Van Den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations?. J. Anim. Ecol. 78, 73–83 (2009).PubMed 
Article 

Google Scholar 
77.Evans, K. et al. Periodic variability in cetacean strandings: links to large-scale climate events. Biol. Let. 1, 147–150 (2005).CAS 
Article 

Google Scholar 
78.Overholtz, W., Hare, J. & Keith, C. Impacts of interannual environmental forcing and climate change on the distribution of Atlantic mackerel on the US Northeast continental shelf. Mar. Coastal Fish. 3, 219–232 (2011).Article 

Google Scholar 
79.Roper, C., Lu, C. & Vecchione, M. A revision of the systematics and distribution of Illex species (Cephalopoda: Ommastrephidae). Smithsonian Contrib. Zool., 405–424 (1998).80.Brodziak, J. & Hendrickson, L. An analysis of environmental effects on survey catches of squids Loligo pealei and Illex illecebrosus in the northwest Atlantic. Fish. Bull. 97, 9–24 (1999).
Google Scholar 
81.Henderson, M. E., Mills, K. E., Thomas, A. C., Pershing, A. J. & Nye, J. A. Effects of spring onset and summer duration on fish species distribution and biomass along the Northeast United States continental shelf. Rev. Fish Biol. Fisheries 27, 411–424 (2017).Article 

Google Scholar 
82.Sosebee, K. A. & Cadrin, S. X. A historical perspective on the abundance and biomass of northeast demersal complex stocks from NMFS and Massachusetts inshore bottom trawl surveys, 1963–2002. (2006).83.Brito-Morales, I. et al. Climate velocity can inform conservation in a warming world. Trends Ecol. Evol. 33, 441–457 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–495 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an r package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evolut. 10, 2195–2202 (2019).Article 

Google Scholar  More

1.Huai, J. J. Dynamics of resilience of wheat to drought in Australia from 1991–2010. Sci. Rep. 7, 9532 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Li, M., Peterson, C. A., Tautges, N. E., Scow, K. M. & Gaudin, A. C. M. Yields and resilience outcomes of organic cover crop, and conventional practices in a Mediterranean climate. Sci. Rep. 9, 12283 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Keersmaecker, W. D. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015).Article 

Google Scholar 
4.Griffith, G. P. et al. Ecological resilience of Arctic marine food webs to climate change. Nat. Clim. Change 9, 868–872 (2019).ADS 
Article 

Google Scholar 
5.You, N. S., Meng, J. J. & Zhu, L. K. Sensitivity and resilience of ecosystems to climate variability in the semi-arid to hyper-arid areas of Northern China: a case study in the Heihe River Basin. Ecol. Res. 33, 161–174 (2018).Article 

Google Scholar 
6.Reijers, V. C. et al. Resilience of beach grasses along a biogeomorphic successive gradient: resource availability vs. clonal integration. Oceologia https://doi.org/10.1007/s00442-019-04568-w (2019).Article 

Google Scholar 
7.Chambers, J. C. et al. Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in clod desert shrublands of western North America. Ecosystems 17, 360–375 (2014).CAS 
Article 

Google Scholar 
8.Driessen, M. M. Fire resilience of a rare, freshwater crustacean in a fire-prone ecosystem and the implications for fire management. Austral Ecol. 44, 1030–1039 (2019).Article 

Google Scholar 
9.Ren, H., Lu, H. F., Li, Y. D. & Wen, Y. G. Vegetation restoration and its research advancement in Southern China. J. Trop. Subtrop. Bot. 27(5), 469–480 (2019).
Google Scholar 
10.Yan, H. M., Zhan, J. Y. & Zhang, T. Review of ecosystem resilience research progress. Prog. Geogr. 31(3), 303–314 (2012).
Google Scholar 
11.Zhan, J. Y., Yan, H. M., Deng, X. Z. & Zhang, T. Assessment of forest ecosystem resilience in Lianhua County of Jiangxi Province. J. Nat. Resour. 27(8), 1304–1315 (2012).
Google Scholar 
12.Pérez-Girón, J. C., Álvarez-Álvarez, P., Díaz-Valera, E. R. & Lopes, D. M. M. Influence of climate variations on primary production indicators and on the resilience of forest ecosystems in a future scenario of climate change: application to sweet chestnut agroforestry systems in the Iberian Peninsula. Ecol. Indic. 113, 106199 (2020).Article 

Google Scholar 
13.Meng, Y. Y. et al. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecol. Inform. 57, 101064 (2020).Article 

Google Scholar 
14.Han, L. et al. Species composition, community structure, and floristic characteristics of desert riparian forest community along the mainstream of Tarim River. Plant Sci. J. 37(3), 324–336 (2019).
Google Scholar 
15.Zhou, H. H. et al. Climate change may accelerate the decline of desert riparian forest in the lower Tarim River, Northwestern China: evidence from tree-rings of Populus euphratica. Ecol. Indic. 111, 105997 (2020).Article 

Google Scholar 
16.Aini, A. et al. Analysis of stakeholders’ cognition on desert riparian forest ecosystem services in the lower reaches of Tarim River, China. Res. Soil Water Conserv. 23(1), 205–209 (2016).
Google Scholar 
17.Li, Y. Q., Chen, Y. N., Zhang, Y. Q. & Xia, Y. Rehabilitating China’s largest inland river. Conserv. Biol. 23(3), 531–536 (2009).PubMed 
Article 

Google Scholar 
18.Dai, J. S. Evaluation of eco-environment and socio-economic benefits on comprehensive reclamation projects on the Tarim River Basin. Doctoral Dissertation of Xinjiang Agricultural University (2015).19.Han, L., Wang, H. Z., Niu, J. L., Wang, J. Q. & Liu, W. Y. Response of Populus euphratica communities in a desert riparian forest to the groundwater level gradient in the Tarim River Basin. Acta Ecol. Sin. 37, 6836–6846 (2017).
Google Scholar 
20.Yang, G. & Guo, Y. P. The change and prospect of vegetation in the end of the lower reaches of Tarim River after ecological water delivery. J. Desert Res. 24(2), 167–172 (2004).
Google Scholar 
21.Yan, H. M., Zhan, J. Y. & Zhang, T. Resilience of forest ecosystems and its influencing factors. Procedia Environ. Sci. 10, 2201–2206 (2011).Article 

Google Scholar 
22.Abenayake, C. C., Mikami, Y., Matsuda, Y. & Jayasinghe, A. Ecosystem service-based composite indicator for assessing community resilience to floods. Environ. Dev. 27, 34–46 (2018).Article 

Google Scholar 
23.Maestas, J. D., Campbell, S. B., Chambers, J. C., Pellant, M. & Miller, R. F. Tapping soil survey information for rapid assessment of sagebrush ecosystem resilience and resistance. Rangelands 38(3), 120–128 (2016).Article 

Google Scholar 
24.Ponce-Campos, G. E. et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
25.Frazier, A. E., Renschler, C. S. & Miles, S. B. Evaluating post-disaster ecosystem resilience using MODIS GPP data. Int. J. Appl. Earth Obs. Geoinform. 21, 43–52 (2013).ADS 
Article 

Google Scholar 
26.Kahiluoto, H. et al. Decline in climate resilience of European wheat. PNAS 116(1), 123–128 (2019).CAS 
PubMed 
Article 

Google Scholar 
27.Li, X. Y. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evolut. 4, 1075–1083 (2020).Article 

Google Scholar 
28.Li, C. H., Zhou, M., Wang, Y. T., Zhu, T. B., Sun, H., Yin, H. H., Cao, H. J., Han, H. Y. Inter-annual variations of vegetation net primary productivity and their spatial-temporal contribution and climate driving in arid Northwest China: a case study of Hexi Corridor. Chin. J. Ecol. (2020).29.Song, J. et al. A global database of plant production and carbon exchange from global change manipulative experiments. Sci. Data 7, 1–7 (2020).Article 
CAS 

Google Scholar 
30.Yang, G. et al. Research progress of ecosystem resilience assessment. Zhejiang Agric. Sci. 60(3), 508–513 (2019).
Google Scholar 
31.Liu, J. Z. & Chen, Y. N. Analysis on converse succession of plant communities at the lower reaches of Tarim River. Arid Land Geogr. 25(3), 231–236 (2002).
Google Scholar 
32.Chen, X., Bao, A. M., Wang, X. P., Guli, J. P. E. & Huang, Y. Recent ecological effectiveness assessment of integrated management projects in the Tarim River. Bull. Chin. Acad. Sci. 32(1), 20–28 (2017).
Google Scholar 
33.Zhao, H., Yan, L. & Ji, F. The dynamics of land utilization in the upper reaches of Tarim River. J. Arid Land Resour. Environ. 15(4), 40–43 (2001).
Google Scholar 
34.Sun, F., Wang, Y. & Chen, Y. N. Dynamics of desert-oasis ecotone and its influencing factors in the Tarim Basin. Chin. J. Ecol. 39(10), 1–11 (2020).
Google Scholar 
35.Xu, G. H. A genetic explanation of the recent changes of ecological environment in the Tarim River Basin, southern Xinjiang. Xinjiang Meteorol. 28–31 (2005).36.Kamkin, A. & Lozinsky, I. Mechanically Gated Channels and Their Regulation (Springer, 2012).Book 

Google Scholar 
37.Feyisa, K. et al. Effects of enclosure management on carbon sequestration, soil properties and vegetation attributes in East African rangelands. CATENA 159, 9–19 (2017).Article 

Google Scholar 
38.Wang, G. H., Ren, Y. J. & Gou, Q. Q. The changes of soil physical and chemical property during the enclosure process in a typical desert oasis ecotone of the Hexi Corridor in northwestern China. J. Desert Res. 40(2), 222–231 (2020).
Google Scholar 
39.Xu, H. L., Ye, M. & Li, J. M. Changes in groundwater levels and the response of natural vegetation to the transfer of water to the lower reaches of the Tarim River. J. Environ. Sci. 19(10), 1199–1207 (2007).Article 

Google Scholar 
40.Zhang, P. F., Guli, J., Bao, A. M., Meng, F. H. & Guo, H. Ecological effects evaluation for short term planning of the Tarim River. Arid Land Geogr. 40(1), 156–164 (2017).
Google Scholar 
41.Gulimire, H., Wang, G. Y., Zhang, Y., Liu, Q. Q. & Su, L. T. Influence mechanisms of intermittent ecological water conveyance on groundwater level and vegetation in arid land. Arid Land Geogr. 41(4), 726–733 (2018).
Google Scholar 
42.Guo, H. W., Xu, H. L. & Ling, H. B. Study of ecological water transfer mode and ecological compensation scheme of the Tarim River Basin in dry years. J. Nat. Resour. 32(10), 1705–1717 (2017).
Google Scholar 
43.Wu, T. Z., Ding, J., Guan, W. K., Ruan, C. J. & Guan, Y. Populus euphratica forest replacement and photosynthetic characteristics in Tarim Populus euphratica national nature reserve. Prot. For. Sci. Technol. 8, 1–4 (2020).
Google Scholar 
44.Zhu, C. G., Aikeremu, A., Li, W. H. & Zhou, H. H. Ecosystem restoration of Populus euphratica forest under the ecological water conveyance in the lower reaches of Tarim River. Arid Land Geography, 44(3), 629–636 (2021).
Google Scholar 
45.Chen, Y. N. Study on Eco-hydrological Problems of the Tarim River Basin in Xinjiang (Science Press, 2010).
Google Scholar 
46.Halik, U., Aishan, T., Betz, F., Kurban, A. & Rouzi, A. Effectiveness and challenges of ecological engineering for desert riparian forest restoration along China’s largest inland river. Ecol. Eng. 127, 11–22 (2019).Article 

Google Scholar 
47.Xinjiang Morning News. In the past three years, the area of the Populus euphratica forest reserve in the Tarim River Basin has increased by 569.95 km2. https://www.sohu.com/a/308626663_100034331?sec=wd (2019).48.China News Service. Ecological water transfer for desert vegetation in lower reaches of Konqi River in Xinjiang. https://news.sina.com.cn/o/2020-02-22/doc-iimxyqvz4945915.shtml (2020). More

Research cruisesThis dataset consists of sequence data from 4 separate cruises: ARK-XXVII/1 (PS80)—17th June to 9th July 2012; Stratiphyt-II— April to May 2011; ANT-XXIX/1 (PS81)—1st to 24th November 2012 and ANT-XXXII/2 (PS103)—16th December 2016 to 3rd February 2017 and covers a transect of the Atlantic Ocean from Greenland to the Weddell Sea (71.36°S to 79.09°N) (Supplementary Table 1). In order to study the composition, distribution and activity of microbial communities in the upper ocean across the broadest latitudinal ranges possible, samples have been collected during four field campaigns as shown in Fig. 1A. The first collection of samples was collected in the North Atlantic Ocean from April to May 2011 by Dr. Willem van de Poll of the University of Groningen, Netherlands and Dr. Klaas Timmermans of the Royal Netherlands Institute for Sea Research. The second set of samples was collected in the Arctic Ocean from June to July 2012, and the third set of samples was collected in the South Atlantic Ocean from October to November 2012. Both of which were collected by Dr. Katrin Schmidt of the University of East Anglia. The final set of samples was collected in the Antarctic Ocean from December 2016 to January 2017 by Dr. Allison Fong of the Alfred-Wegener Institute for Polar and Marine Research, Bremerhaven, Germany.SamplingWater samples from the Arctic Ocean and South Atlantic Ocean expeditions were collected using 12 L Niskin bottles (Rosette sampler with an attached Sonde (CTD, conductivity, temperature, depth) either at the chlorophyll maximum (10–110 m) and/or upper of the ocean (0–10 m). As soon as the rosette sampler was back on board, water samples were immediately transferred into plastic containers and transported to the laboratory. All samples were accompanied by measurements on salinity, temperature, sampling depth and silicate, nitrate, phosphate concentration (Supplementary Table 1). Water samples were pre-filtered with a 100 μm mesh to remove larger organisms and subsequently filtered onto 1.2 μm polycarbonate filters (Isopore membrane, Millipore, MA, USA). All filters were snap frozen in liquid nitrogen and stored at −80 °C until further analysis.Water samples from the North Atlantic Ocean cruise were also taken with 12 L Niskin bottles attached to a Rosette sampler with a Sonde. However, these samples were filtered onto 0.2 μm polycarbonate filters (Isopore membrane, Millipore, MA, USA) without pre-filtration but snap frozen in liquid nitrogen and stored at −80 °C as the other samples.Water samples from the Southern Ocean cruise were taken with 12 L Niskin bottles attached to an SBE911plus CTD system equipped with 24 Niskin samplers. These samples were filtered onto 1.2 μm polycarbonate membrane filters (Merck Millipore, Germany) in a container cooled to 4 °C and snap frozen in liquid nitrogen and stored at −80 °C as the other samples. Environmental data recorded at the time of sampling can be found in Supplementary Table 1.DNA extractions: Arctic Ocean and South Atlantic Ocean samplesDNA was extracted with the EasyDNA Kit (Invitrogen, Carlsbad, CA, USA) with modification to optimise DNA quantity and quality. Briefly, cells were washed off the filter with pre-heated (65 °C) Solution A and the supernatant was transferred into a new tube with one small spoon of glass beads (425–600 μm, acid washed) (Sigma-Aldrich, St. Louis, MO, USA). Samples were vortexed three times in intervals of 3 s to break the cells. RNase A was added to the samples and incubated for 30 min at 65 °C. The supernatant was transferred into a new tube and Solution B was added followed by a chloroform phase separation and an ethanol precipitation step. DNA was pelleted by centrifugation and washed several times with isopropanol, air dried and suspended in 100 μL TE buffer (10 mM Tris-HCl, pH 7.5, 1 mM EDTA, pH 8.0). Samples were snap frozen in liquid nitrogen and stored at −80 °C until sequencing.DNA extractions: North Atlantic Ocean samplesNorth Atlantic Ocean samples were extracted with the ZR-Duet™DNA/RNA MiniPrep kit (Zymo Research, Irvine, USA) allowing simultaneous extraction of DNA and RNA from one sample filter. Briefly, cells were washed from the filters with DNA/RNA Lysis Buffer and one spoon of glass beads (425–600 μm, Sigma-Aldrich, MO, USA) was added. Samples were vortexed quickly and loaded onto Zymno-Spin™IIIC columns. The columns were washed several times and DNA was eluted in 60 μmL, DNase-free water. Samples were snap frozen in liquid nitrogen and stored at −80 °C until sequencing.DNA extractions: Southern Ocean samplesDNA from the Southern Ocean samples was extracted with the NucleoSpin Soil DNA extraction kit (Macherey‐Nagel) following the manufacturer’s instructions. Briefly, cells were washed from the filters with DNA Lysis Buffer and into a lysis tube containing glass beads was added. Samples were disrupted by bead beating for 2 × 30 s interrupted by 1 min cooling on ice and loaded onto the NucleoSpin columns. The columns were washed three times and DNA was eluted in 50 μL, DNase-free water. Samples were stored at −20 °C until further processing.Amplicon sequencing of 16S and 18S rDNAAll extracted DNA samples were sequenced and pre-processed by the Joint Genome Institute (JGI) (Department of Energy, Berkeley, CA, USA). iTAG amplicon sequencing was performed at JGI with primers for the V4 region of the 16S (FW(515F): GTGCCAGCMGCCGCGGTAA; RV(806R): GGACTACNVGGGTWTCTAAT)49 and 18S (FW(565F): CCAGCASCYGCGGTAATTCC; RV(948R): ACTTTCGTTCTTGATYRA)50. (Supplementary Table 6) rRNA gene (on an Illumina MiSeq instrument with a 2 × 300 base pairs (bp) read configuration51. 18S sequences were pre-processed, this consisted of scanning for contamination with the tool Duk (US Department of Energy Joint Genome Institute (JGI), 2017,a) and quality trimming of reads with cutadapt52. Paired end reads were merged using FLASH53 with a max mismatch set to 0.3 and min overlap set to 20. A total of 54 18S samples passed quality control after sequencing. After read trimming, there was an average of 142,693 read pairs per 18S sample with an average length of 367 bp and 2.8 Gb of data over all samples.16S sequences were pre-processed, this consisted of merging the overlapping read pairs using USEARCH’s merge pairs54 with the parameter minimum number of differences (merge max diff pct) set to 15.0 into unpaired consensus sequences. Any reads that could not be merged are discarded. JGI then applied the tool USEARCH’s search oligodb tool with the parameters mean length (len mean) set to 292, length standard deviation (len stdev) set to 20, primer trimmed max difference (primer trim max diffs) set to 3, a list of primers and length filter max difference (len filter max diffs) set to 2.5 to ensure the Polymerase Chain Reaction (PCR) primers were located with the correct direction and inside the expected spacing. Reads that did not pass this quality control step were discarded. With a max expected error rate (max exp err rate) set to 0.02, JGI evaluated the quality score of the reads and those with too many expected errors were discarded. Any identical sequence was de-duplicated. These are then counted and sorted alphabetically for merging with other such files later. A total of 57 × 16S samples passed quality control after sequencing. There was an average 393,247 read pairs per sample and an average base length of 253 bp for each sequence with a total of 5.6 Gb.RNA extractions: Arctic Ocean and Atlantic samplesRNA from the Arctic and Atlantic Ocean samples was extracted using the Direct-zol RNA Miniprep Kit (Zymo Research, USA). Briefly, cells were washed off the filters with Trizol into a tube with one spoon of glass beads (425–600 μm, Sigma-Aldrich, MO, USA). Filters were removed and tubes bead beaten for 3 min. An equal volume of 95% ethanol was added, and the solution was transferred onto Zymo-Spin™ IICR Column and the manufacturer instructions were followed. Samples were treated with DNAse to remove DNA impurities, snap frozen in liquid nitrogen and stored at −80 °C until sequencing.RNA extractions: Southern OceanRNA from the Southern Ocean samples was extracted using the QIAGEN RNeasy Plant Mini Kit (QIAGEN, Germany) following the manufacturer’s instructions with on-column DNA digestion. Cells were broken by bead beating like for the DNA extractions before loading samples onto the columns. Elution was performed with 30 µm RNase-free water. Extracted samples were snap frozen in liquid nitrogen and stored at −80 °C until sequencing.Metatranscriptome sequencingAll samples were sequenced and pre-processed by the U.S. Department of Energy Joint Genome Institute (JGI). Metatranscriptome sequencing was performed on an Illumina HiSeq-2000 instrument27. A total of 79 samples passed quality control after sequencing with 19.87 Gb of sequence read data over all samples for analysis. This comprised a total of 34,241,890 contigs, with an average length of 503 and an average GC% of 51%. This resulted in 36354419 of non-redundant genes detected.JGI employed their suite of tools called BBTools55 for preprocessing the sequences. First, the sequences were cleaned using Duk a tool in the BBTools suite that performs various data quality procedures such as quality trimming and filtering by kmer matching. In our dataset, Duk identified and removed adaptor sequences, and also quality trimmed the raw reads to a phred score of Q10. In Duk the parameters were; kmer-trim (ktrim) was set to r, kmer (k) was set to 25, shorter kmers (mink) set to 12, quality trimming (qtrim) was set to r, trimming phred (trimq) set to 10, average quality below (maq) set to 10, maximum Ns (maxns) set to 3, minimum read length (minlen) set to 50, the flag “tpe” was set to t, so both reads are trimmed to the same length and the “tbo” flag was set to t, so to trim adaptors based on pair overlap detection. The reads were further filtered to remove process artefacts also using Duk with the kmer (k) parameter set to 16.BBMap55 is another a tool in the BBTools suite, that performs mapping of DNA and RNA reads to a database. BBMap aligns the reads by using a multi-kmer-seed-and-extend approach. To remove ribosomal RNA reads, the reads were aligned against a trimmed version of the SILVA database using BBMap with parameters set to; minratio (minid) set to 0.90, local alignment converter flag (local) set to t and fast flag (fast) set to t. Also, any human reads identified were removed using BBMap.BBmerge56 is a tool in the BBTools suite that performs the merging of overlapping paired end reads (Bushnell, 2017). For assembling the metatranscriptome, the reads were first merged with the tool BBmerge, and then BBNorm was used to normalise the coverage so as to generate a flat coverage distribution. This type of operation can speed up assembly and can even result in an improved assembly quality.Rnnotator52 was employed for assembling the metatranscriptome samples 1–68. Rnnotator assembles the transcripts by using a de novo assembly approach of RNA-Seq data and it accomplishes this without a reference genome52. MEGAHIT57 was employed for assembling the metatranscriptome samples 69–82. The tool BBMap was used for reference mapping, the cleaned reads were mapped to metagenome/isolate reference(s) and the metatranscriptome assembly.Metatranscriptome analysisJGI performed the functional analysis on the metatranscriptomic dataset. JGI’s annotation system is called the Metagenome Annotation Pipeline (MAP) (v4.15.2)27. JGI used HMMER 3.1b258 and the Pfam v3059 database for the functional analysis of our metatranscriptomic dataset. This resulted in 11,205,641 genes assigned to one or more Pfam domain. This resulted in 8379 Pfam functional assignments and their gene counts across the 79 samples. The files were further normalised by applying hits per million.18S rDNA analysisA reference dataset of 18S rRNA gene sequences that represent algae taxa was compiled for the construction of the phylogenetic tree by retrieving sequences of algae and outgroups taxa from the SILVA database (SSUREF 115)60 and Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) database61. The algae reference database consists of 1636 species from the following groups: Opisthokonta, Cryptophyta, Glaucocystophyceae, Rhizaria, Stramenopiles, Haptophyceae, Viridiplantae, Alveolata, Amoebozoa and Rhodophyta. A diagram of the 18S classification pipeline can be found in Supplementary Fig. 1. In order to construct the algae 18S reference database, we first retrieved all eukaryotic species from the SILVA database with a sequence length of  > = 1500 base pairs (bp) and converted all base letters of U to T. Under each genus, we took the first species to represent that genus. Using a custom written script (https://github.com/SeaOfChange/SOC/blob/master/get_ref_seqs.pl), the species of interest (as stated above) were selected from the SILVA database, classified with NCBI taxa IDs and a sequence information file produced that describes each of the algae sequences by their sequence ID and NCBI species ID. Taxonomy from the NCBI database, eukaryote sequences from the SILVA database and a list of algal taxa including outgroups were used as input for the script. This information was combined with the MMETSP database excluding duplications.The algae reference database was clustered to remove closely related sequences with CD-HIT (4.6.1)62 using a similarity threshold of 97%. Using ClustalW (2.1)63 we aligned the reference sequences with the addition of the parameter iteration numbers set to 5. The alignment was examined by colour coding each species to their groups and visualising in iTOL64. It was observed that a few species were misaligning to other groups and these were then deleted using Jalview65. The resulting alignment was tidied up with TrimAL (1.1)66 by applying parameters to delete any positions in the alignment that have gaps in 10% or more of the sequence, except if this results in less than 60% of the sequence remaining. A maximum likelihood phylogenetic reference tree and statistics file based on our algae reference alignment was constructed by employing RaxML (8.0.20)67 with a general time reversible model of nucleotide substitution along with the GAMMA model of rate heterogeneity. For a description of the lineages of all species back to the root in the algae reference database, the taxa IDs were submitted for each species to extract a subset of the NCBI taxonomy with the NCBI taxtastic tool (0.8.4)68 Based on the algae reference multiple sequence alignment, with HMMER3 (3.1B1)69 a Profile HMM was created. A pplacer reference package using taxtastic was generated, which produced an organized collection of all the files and taxonomic information into one directory. With the reference package, a SQLite database was created using pplacer’s Reference Package PReparer (rppr). With hmmalign, the query sequences were aligned to the reference set and created a combined Stockholm format alignment. Pplacer (re-aligned to the reference set and created a combined Stockholm format alignment. Pplacer (1.1)70 was used to place the query sequences on the phylogenetic reference tree by means of the reference alignment according to a maximum likelihood model70 The place files were converted to CSV with pplacer’s guppy tool; in order to easily take those with a maximum likelihood score of  > = 0.5 and counted the number of reads assigned to each classification. This resulted in 6,053,291 reads that were taxonomically assigned being taken for analysis.Normalisation of 18S rDNA gene copy number18S rDNA gene copy number vary widely among eukaryotes. In order to create an estimate of abundances of the species in the samples the data had to be normalised. Previous work has explored the link between copy number and genome size71. However, there is not a single database of 18S rDNA gene copy numbers for eukaryote species. In order to address this, gene copy number and related genome sizes of 185 species across the eukaryote tree was investigated and plotted (Supplementary Fig. 2, Supplementary Table 4)68,71,72,73,74,75,76,77,78,79. Based on the log transformed data, a significant correlation with a R2 of 0.55 with a p-value  More

Ethical statementThe present study was discussed and approved by the ethics and animals’ welfare committee of the University of Veterinary Medicine, Vienna, Austria, in accordance with good scientific practice and national legislation (GZ: BMWFW-68.205/0151-WF/V/3b/2016 and GZ: BMWFW-68.205/0224-WF/V/3b/2016). All methods were carried out in accordance with relevant guidelines and regulations. We confirm that the study was carried out in compliance with the ARRIVE guidelines. No plants or plant parts were used in this study.Animals and study areaThe study animals were kept in an outdoor enclosure (~ 55 ha, for details see “Supplementary Material”). The study enclosure was covered with a deciduous forest, mainly Turkey oak (Quercus cerris) and pubescent oak (Quercus pubescens) and included only few meadow patches. For the present study ten adult females, were used. We concentrated on females only because the live capture and handling of males are hampered by the large size and ferocity of boars. Also, due to competition and high levels of aggression between males during rut, the stocking of the enclosure was strongly female biased. During the study period (12/2016–01/2019), the animal density was ~ 1 adult female/ha plus up to 20 males (total) of different ages. Due to this relatively high density, animals were supplemented with 1–1.5 kg corn/individual once a day (at 2:00–14:00 h) at two feeding areas, each ~ 40 × 20 m. The enclosure was part of a game reserve, which was enclosed by 2.5 m high, solid, non-transparent fencing and was closed for the public. Thus, the study site provided an environment without disturbances due to hikers, bikers or straying dogs. There were no battue hunts or other disturbances due to hunting or forest management activities during the study period in the enclosure.Animals were trapped once a year in autumn within the feeding sites to collect data on reproductive success and body condition of females and to separate some of them for implantation/explantation of loggers. While feeding, we closed the access gates and released the boars one by one trough a wooden corridor back into the enclosure. While in the wooden corridor we recorded the body mass of each individual (Gallagher SmartScale® 500, Groningen, Netherlands). Due to management reasons the juveniles (born in spring) were removed from the enclosure during this procedure.Implantation of temperature and heart rate loggersWe implanted a heart rate logger (DST centi-HRT, Star-Oddi, Gardabaer, Iceland) and two custom-built temperature loggers in each of ten female wild boars in October/November 2016 and 2017 (age 5 and 6 years). All details about surgery techniques and anaesthesia protocols are provided in the “Supplementary Material”. Explantations were carried out approximately one year after implantations. The last explanation was carried out in January 2019. One female was implanted in two consecutive years. Mean body mass at date of implantation for all females was 71.8 ± 15.5 kg.The heart rate logger was adjusted to record data at a time interval of 12 min to cover one year of data recording. To remove outliers, all initial data from these recorders were subjected to a running median over five consecutive values. The HR recorder was positioned subcutaneously, in proximity to the heart on the lateral rib cage, behind the moving area of the elbow, to avoid rubbing, or inserted and tethered into the ventral subperitoneal space caudal of the xiphoid process of the sternum.The self-built temperature loggers were covered with inert surgical wax and had a weight of ~ 8 g. Time interval of recording was 4 min, the accuracy 0.01 °C. One of the two temperature loggers had an especially flat shape (3.4 × 1.9 × 0.5 cm) to fit smoothly into the subcutaneous neck region. The second temperature logger was placed into the intraperitoneal cavity, tethered at the Linea alba (diameter = 2.1 cm, height = 1.2 cm). For details on surgery, see “Supplement”.We collected and evaluated a mean of 227.45 ± 160.69 days of heart rate recording per individual (SD, n = 11: 33 days, 58 days, 79 days, 89 days, 143 days, 189 days, 272 days, 345 days, 412 days, 421 days, 461 days), and a mean of 382.00 ± 100.17 days (SD), of subcutaneous logger recording per individual (n = 8: 143 days, 363 days, 411 days, 414 days, 419 days, 421 days, 424 days, 461 days). From the loggers implanted in the abdominal cavity we collected 338.71 ± 117.01 days (SD) per individual (n = 10: 140 days, 143 days, 363 days, 364 days, 411 days, 419 days, 421 days, 421 days, 424 days, 461 days). The hourly means of monitored heart rates of each animal over the course of the year are shown in Supplementary Fig. S1.Activity dataTo record the activity of animals, a telemetry system (Smartbow System, Zoetis, New Jersey, USA) was installed around the two neighbouring feeding areas and two close water ponds in the enclosure. The system consisted of a central solar power and computing station and ten receivers located at the height of 2–3 m. Part of the system were ear-tags (34 g; 52 mm × 36 mm × 17 mm, for details see “Supplementary Material”). The accelerometer (located inside ear-tags) measured triaxial acceleration (x, y, z). As an estimate of locomotor activity (ACT), we computed the total acceleration vector from sqrt (x2 + y2 + z2).Climate and mastThe study site in Eastern Austria (altitude 130 m) is generally characterised by a Pannonian climate. According to long-term climate records, the mean annual temperature is 10 °C in combination with a mean precipitation of 600–700 mm and 1898 h of sunshine per year (ZAMG, 1971–2000).We recorded ambient temperature (Ta) and black bulb temperature (Tab) at 2 m height directly at the study site (Vantage Pro 2 with black bulb extension, Davis Instruments, Hayward, USA).To assess the extent of the acorn mast, each autumn seven nets, 4 × 4 m, were set up to collect acorns at random locations. The nets were regularly emptied between Sept. and Nov. each year, and the collected acorns were dried and weighed. In the autumns prior to the study (2016) and during both full study years (2017/2018) there was seeding of at least part of the oaks. Over ~ 90 days in each autumn we collected 52.4 g/m2, 134.8 g/m2, and 37.5 g/m2 acorn in 2016, 2017, and 2018, respectively. Thus, 2017 was a full mast year but there were acorns available in autumn throughout the study period.Data analysisTo facilitate handling of data and to reduce autocorrelation we compiled and evaluated hourly means for all data, i.e., heart rates (HR; see Suppl. Fig. S1), intraperitoneal and subcutaneous body temperature (Tbip and Tbsc, respectively) and activity (ACT), as well as ambient air temperature (Ta) and black-bulb temperature (Tab). We further tested for effects of day of year (DOY) and hour of day (HOUR). We did not assess the influence of environmental conditions in different years, because due to logger-failures and thus scarcity of heart rates, all data were pooled for different years (with similarly warm conditions and food available year-round). Also, we did not further evaluate daily rhythms, because animals were always fed in the early afternoon, which may have influenced their timing.We investigated the effects of season (DOY), hour of day (HOUR), and Ta on the response variables HR, Tbip, Tbsc, and ACT. We additionally used Tbip, Tbsc, and ACT as predictors for HR. As many of the relationships between these were non-linear, we used general additive mixed models (GAMMs), as implemented in package mgcv60 in R61. This function fits non-linear splines to the data, which are penalized for their “wiggliness”, i.e., the number of turning points in the fit. Because the data were repeated measurements, we calculated for all response variables mixed models with an intercept for each animal ID as a random factor (using s (ID, bs = ”re”)). Hence, these mixed models allowed for differences in the mean level of heart rates, temperatures and activities, between individuals. All residuals of models were approximately normally distributed, as inspected by normal quantile–quantile plots. Hourly means of the response variables contained various degrees of autocorrelation. This was corrected by including autoregressive order 1 (AR1) error models in GAMM-functions, which successfully reduced the autocorrelation at lag 1 to nonsignificant levels. This was confirmed by comparing the autocorrelation function of model residuals (ACF) before and after their correction. To illustrate the effects of independent variables, we show population-level predictions from GAMMs. These graphs contain rug plots to illustrate the distribution of independent variables. Because these plots were too dense for all original data (resulting in black bars), we show uniform random samples (n = 1000) from each independent predictor variable.Because hourly mean data consisted of ~ 117,000 observations we used the mgcv function “bam”, which uses numerical methods designed for large datasets. To fit non-linear functions to predictors, we used the default thin plate splines. Only the cyclic variables DOY and HOUR were modelled using cubic cyclic splines, which are guaranteed to have identical start- and endpoints (e.g., at Jan 1 and Dec 31). GAMMs were always fitted using method REML. As Tbip and Tbsc were only moderately correlated (r = 0.30), both were entered simultaneously as independent variables in the model on heart rate.We did not use partial regression plots from multiple regressions that included activity. This is because activity could only be recorded partly, in the vicinity of telemetry receivers. Thus, models that include ACT as well as all other predictors simultaneously, were restricted to ~ 7% of the data. However, we still used a full multiple regression model HR for the purpose of assessing relative variable importance (of DOY, HOUR, Ta, Tbip, Tbsc, and ACT). F-values from this model provide an indication of the importance of different predictors.To model a possible role of solar radiation and basking we computed the difference between Tab and Ta, called Tdiff, which represents an index of radiation. We used again GAMMs to test if Tdiff would affect Tbip, Tbsc and HR after adjusting for effects of Ta, hour of day, and the random factor animal ID.For a comparison of species we also computed monthly means and SEMs of HR in wild boars, and created a graph of seasonal time courses in other ungulates as published in Arnold2 that were kindly provided by the author. If not stated otherwise we provide means ± SEM. More

1.Pradhan, S., Saha, G. K. & Khan, J. A. Food habits of the red panda, Ailurus fulgens, in the Singalila National Park, Darjeeling, India. J. Bombay Nat. Hist. Soc. 98, 224–230 (2001).
Google Scholar 
2.Bista, D. et al. Distribution and habitat use of red panda in the Chitwan–Annapurna Landscape of Nepal. PLoS ONE 12, e0178797 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Martin, P. S. The discovery of America. Science 179, 969–974 (1973).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Miller, G. H. et al. Pleistocene extinction of Genyornis newtoni: human impact on Australian megafauna. Science 283, 205–208 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Grayson, D. K. & Meltzer, D. J. A requiem for North America overkill. J. Archaeol. Sci. 30, 585–593 (2003).Article 

Google Scholar 
6.van der Kaars, S. et al. Humans rather than climate the primary cause of Pleistocene megafaunal extinction in Australia. Nat. Commun. https://doi.org/10.1038/ncomms14142 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Louys, J. & Roberts, P. Environmental drivers of megafaunal and hominin extinction in Southeast Asia. Nature 586, 402–406 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Ripple, W. J. et al. Tertiary fossil fungi from Kiandra, New South Wales. Proc. Linn. Soc. NSW. 97, 141–149 (1975).
Google Scholar 
9.Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Brook, S. M. et al. Lessons learned from the loss of a flagship: the extinction of the Javan rhinoceros Rhinoceros sondaicus annamiticus from Vietnam. Biol. Conserv. 174, 21–29 (2014).Article 

Google Scholar 
11.Prasad, V., Stromberg, C. A. E., Alimohammadian, H. & Sahni, A. Dinosaur coprolites and the early evolution of grasses and grazers. Science 310, 1177–1180 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Shillito, L. M., Blong, J. C., Green, E. J. & VanAsperen, E. N. The what, how and why of archaeological human coprolite analysis. Earth Sci. Rev. 207, 103196 (2020).CAS 
Article 

Google Scholar 
13.van Geel, B. et al. The ecological implications of a Yakutian mammoth’s last meal. Quat. Res. 69, 361–376 (2008).Article 
CAS 

Google Scholar 
14.Rawlence, N. J., Wood, J. R., Bocherens, H. & Rogers, K. M. Dietary interpretations for extinct megafauna using coprolites, intestinal contents and stable isotopes: Complimentary or contradictory?. Quat. Sci. Rev. 142, 173–178 (2016).ADS 
Article 

Google Scholar 
15.Carrion, J. S. Pleistocene landscape in central Iberia inferred from pollen analysis of hyena coprolite. J. Quat. Sci. 22(2), 191–202 (2007).Article 

Google Scholar 
16.Wood, J. R. et al. Coprolite deposits reveal the diet and ecology of the extinct New Zealand megaherbivore moa (Aves, Dinornithiformes). Quat. Sci. Rev. 27, 2593–2602 (2008).ADS 
Article 

Google Scholar 
17.Gravendeel, B. et al. Multiproxy study of the last meal of a mid-Holocene Oyogos Yar horse, Sakha Republic, Russia. The Holocene 24(10), 1288–1296 (2014).ADS 
Article 

Google Scholar 
18.Akeret, O., Haas, J. N., Leuzinger, U. & Jacomet, S. Plant macrofossils and pollen in goat/sheep faeces from the Neolithic lake-shore settlement Arbon Bleiche 3, Switzerland. The Holocene 9(2), 175–182 (1999).ADS 
Article 

Google Scholar 
19.Birks, H. H. et al. Evidence for the diet and habitat of two late Pleistocene mastodons from the Midwest, USA. Quat. Res. 79, 1–21 (2018).ADS 

Google Scholar 
20.van der Waal, C. et al. Large herbivores may alter vegetation structure of semi-arid savannas through soil nutrient mediation. Oecologia 165, 1095–1107 (2011).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Velazquez, N. J. & Burry, L. S. Palynological analysis of Lama guanicoe modern feces and its importance for the study of coprolites from Patagonia, Argentina. Rev. Palaeob. Palynol. 184, 14–23 (2012).Article 

Google Scholar 
22.Basumatary, S. K., McDonald, H. G. & Gogoi, R. Pollen and non-pollen palynomorph preservation in the dung of the Greater one –horned rhino (Rhinoceros unicornis), and its implication to palaeoecology and palaeodietary analysis: a case study from India. Rev. Palaeo. Palynol. 244, 153–162 (2017).Article 

Google Scholar 
23.Basumatary, S. K., Singh, H., McDonald, H. G., Tripathi, S. & Pokharia, A. K. Modern botanical analogue of endangered Yak (Bos mutus) dung from India: Plausible linkage with living and extinct megaherbivores. PLoS ONE 14(3), e0202723 (2019).24.Roberts, M. S. & Gittleman, J. L. Ailurus fulgens. Mammalian species. Am. Soc. Mammal. 222, 1–8 (1984).
Google Scholar 
25.Johnson, K. G., Schaller, G. B. & Hu, J. C. Comparative behavior of red and giant pandas in the Wolong Reserve, China. J. Mammal. 69, 552–564 (1988).Article 

Google Scholar 
26.Yonzon, P. B. & Hunter, M. L. Ecological study of the red panda in Nepal-Himalaya. red panda Biology 1, 7 (1989).
Google Scholar 
27.Wei, F. W., Wang, W., Zhou, A., Hu, J. & Wei, Y. Preliminary study on food selection and feeding strategy of red pandas. Acta Theriol. Sin. 15, 259–266 (1995).
Google Scholar 
28.Zhang, Z. J., Hu, J. C., Yang, J. D., Li, M. & Wei, F. W. Food habits and space-use of red panda, Ailurus fulgens in the Fengtongzhai Nature Reserve, China: Food effects and behavioural response. Acta Theriol. 54, 225–234 (2009).Article 

Google Scholar 
29.Dorji, S., Vernes, K. & Rajaratnam, R. Habitat correlates of the red panda in the temperate forests of Bhutan. PLoS ONE 6, e26483 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Panthi, S., Aryal, A., Raubenheimer, D., Lord, J. & Adhikari, B. Summer diet and distribution of the Red Panda (Ailurus fulgens fulgens) in Dhorpatan Hunting Reserve, Nepal. Zool. Stud. 51(5), 701–709 (2012).
Google Scholar 
31.Sharma, H. P., Swenson, J. E. & Belant, J. L. Seasonal food habits of the red panda (Ailurus fulgens) in Rara National Park, Nepal. Hystrix 25(1), 47–50 (2014).
Google Scholar 
32.Panthi, S., Coogan, S. C. P., Aryal, A. & Raubenheimer, D. Diet and nutrient balance of red panda in Nepal. Sci. Nat. 102, 54 (2015).Article 
CAS 

Google Scholar 
33.Thapa, A. & Basnet, K. Seasonal diet of wild red panda (Ailurus fulgens) in Langtang national park, Nepal Himalaya. Inter. J. Conser. Sci. 6(2), 261–270 (2015).CAS 

Google Scholar 
34.Thapa, A. et al. The endangered red panda in Himalayas: potential distribution and ecological habitat associates. Glob. Ecol. Conser. 21, e00890 (2020).35.Hu, Y. et al. Genomic evidence for two phylogenetic species and long-term population bottlenecks in red pandas. Sci. Adv. 6, eaax5751 (2020).36.IUCN. IUCN red list of threatened species. Version 2018.1. [Online] Available: www.iucnredlist.org (August 14, 2018).37.Salesa, M. J., Peigne, S., Antón, M. & Morales, J. Evolution of the Family Ailuridae: Origins and Old- World Fossil Record. In Red Panda: Biology and Conservation of the First Panda (ed. Glatston, A. R.) 27–41 (Elsevier, 2011).Chapter 

Google Scholar 
38.Thapa, A. et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8, 10542–10554 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Chaudhury, A. An overview of the status and conservation of the red panda (Ailurus fulgens) in India, with reference to its global status. Oryx 35(3), 250–259 (2001).Article 

Google Scholar 
40.Eizirik, E. et al. Pattern and timing of diversification of the mammalian order carnivora inferred from multiple nuclear gene sequences. Mol. Phylogenet. Evol. 56(1), 49–63 (2015).Article 
CAS 

Google Scholar 
41.Hu, Y. et al. Comparative genomics reveals convergent evolution between bamboo-eating giant and red pandas. Proc. Natl. Acad. Sci. 114(5), 1081–1086 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Jha, A. K. Release and reintroduction of captive-bred red pandas into Singalila National Park, Darjeeling, India. In Red panda: biology and conservation of the first panda (ed. Glatson, A. R.) 435–446 (Academic Press, 2011).Chapter 

Google Scholar 
43.Wikramanayake, E., E. Terrestrial Ecoregions of the Indo-Pacific: A Conservation Assessment. Washington, D.C.: Island Press. ISBN 1-55963-923-7 (2002).44.Janzen, D. H. Why bamboos wait so long to flower. Ann. Rev. Eco. Syst. 7, 347–391 (1976).Article 

Google Scholar 
45.van Geel, B. et al. Giant deer (Megaloceros giganteus) diet from Mid-Weichselian deposits under the present North Sea inferred from molar-embedded botanical remains. J. Quat. Sci. 33, 924–933 (2018).Article 

Google Scholar 
46.Basumatary, S. K. & McDonald, H. G. Coprophilous fungi from dung of the greater one-horned Rhino in Kaziranga National Park, India and its implication to palaeoherbivory and palaeoecology. Quat. Res. 88, 14–22 (2017).Article 

Google Scholar 
47.Swati, T. et al. Multiproxy studies on dung of endangered sangai (Rucervus eldii eldii) and Hog deer (Axis porcinus) from Manipur, India: Implication for paleoherbivory and paleoecology. Rev. Palaeob. Palyn. 263, 85–103 (2019).Article 

Google Scholar 
48.Goh, T. K., Ho, W. H., Hyde, K. D., Whitton, S. R. & Umali, T. E. New records and species of Canalisporium (Hyphomycetes), with a revision of the genus. Canadian J. Bot. 76, 142–152 (1998).
Google Scholar 
49.Heudre, D., Wetzel, C. E., Moreau, L. & Ector, L. Sellaphora davoutiana sp. Nov.: a new freshwater diatom species (Sellaphoraceae, Bacillariophyta) in lakes of Northeastern France. Phytotaxa 346(3), 269–279 (2018).Article 

Google Scholar 
50.Biswas, O. et al. Can grass phytoliths and indices be relied on during vegetation and climate interpretations in the eastern Himalayas? Studies from Darjeeling and Arunachal Pradesh, India. Quat. Sci. Rev. 134, 114–132 (2016).ADS 
Article 

Google Scholar 
51.Biswas, O. et al. A comprehensive calibrated phytolith based climatic index from the Himalaya and its application in palaeotemperature reconstruction. Sci. Total Environ. 750, 142 (2021).Article 
CAS 

Google Scholar 
52.Chaudhuri, A. B. Common grasses and sedges of Kurseong, Kalimpong and Darjeeling forest divisions, West Bengal. Indian For. 86(6), 336–348 (1960).
Google Scholar 
53.Hajra, P. K. & Verma, D. M. Flora of Sikkim, Vol. II. Botanical Survey of India, (1996).54.Neto, M. A. M. & Guerra, M. P. A new method for determination of the photosynthetic pathway in grasses. Photosyn. Res. 142, 51–56 (2019).CAS 
Article 

Google Scholar 
55.Frank, K., Bruckner, A., Hilpert, A., Heethoft, M. & Bluthgen, N. Nutrient quality of vertebrate dung as a diet for dung beetles. Sci. Rep. 17, 12141 (2017).ADS 
Article 
CAS 

Google Scholar 
56.Tieszen, L. L. Natural variations in the carbon isotope values of plants: implications for archaeology, ecology, and palaeoecology. J. Archaeol. Sci. 78, 227–248 (1991).Article 

Google Scholar 
57.Heaton, T. Spatial, species, and yemporal variations in the 13C/12C ratios of C3 plants: Implications for palaeodiet studies. J. Archaeol. Sci. 26, 637–649 (1999).Article 

Google Scholar 
58.Arens, N. C., Jahren, A. H. & Amundson, R. Can C3 plants faithfully record the carbon isotopic composition of atmospheric carbon dioxide?. Paleobiology 26(1), 137–164 (2000).Article 

Google Scholar 
59.Cerling, T. E., Harris, J. M. & Leakey, M. G. Browsing and grazing in modern and fossil proboscideans. Oecologia 120, 364–374 (1999).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Mac Fadden, B. J., Cerling, T. E., Harries, J. M. & Prado, J. L. Ancient latitudinal gradients of C3/C4 grasses interpreted from stable isotopes of New World Pleistocene horse (Equus) teeth. Global Ecol. Biog. 8, 137–149 (1999).
Google Scholar 
61.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late Holocene extinctions in Madagascar. Proc. Natl Acad. Sci. U.S.A. 100(19), 10800–10805 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Davis, O. K. & Shafer, D. S. Sporormiella fungal spores, a palynological means of detecting herbivore density. Palaeog. Palaeoclim. Palaeo. 237, 40–50 (2006).ADS 
Article 

Google Scholar 
63.Raper, D. & Bush, M. A test of Sporormiella representation as a predictor of megaherbivore presence and abundance. Quat. Res. 71, 490–496 (2009).Article 

Google Scholar 
64.Perrotti, A. G. & Van Asperen, E. N. 2019: Dung fungi as a proxy for megaherbivores: opportunities and limitations for archaeological applications. Veget. Hist. Archaeobot. 28, 93–104 (2019).Article 

Google Scholar 
65.Ingold, C. T. Ballistics in certain ascomycetes. New Phytol. 60, 143–149 (1961).Article 

Google Scholar 
66.Trail, F. Fungal cannons: explosive spore discharge in the Ascomycota. FEMS Microbio. Letters 276, 12–18 (2007).CAS 
Article 

Google Scholar 
67.Yafetto, L. The fastest flights in nature: high-speed spore discharge mechanisms among fungi. PLoS ONE 3, e3237 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Erdtman, G. An introduction to Pollen Analysis (Waltham, 1953).
Google Scholar 
69.Gupta, H.P. & Sharma, C. Pollen flora of North-west Himalaya. Indian Association of Palynostratigraphers, Lucknow, India, (1986).70.Van Geel, B. Environmental reconstruction of a Roman Period settlement site in Uitgeest (The Netherlands), with special reference to coprophilous fungi. J. Archaeo. Sci. 30, 873–883 (2003).Article 

Google Scholar 
71.Van Asperen, E. N., Kirby, J. R. & Hunt, C. O. The effect of preparation methods on dung fungal spores: Implications for recognition of megafaunal populations. Rev. Palaeobot. Palynol. 229, 1–8 (2016).Article 

Google Scholar 
72.Neumann, K. International code for phytolith nomenclature ICPN 2.0. Ann. Bot. 124, 189–199 (2019).Article 

Google Scholar 
73.Hill, M. O. & Gauch, H. G. Detrended correspondence analysis, an improved ordination technique. Vegetatio 42(1), 47–58 (1980).Article 

Google Scholar 
74.Ter Braak, C. J. F. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

Google Scholar 
75.Ter Braak, C. J. F. Canoco-a FORTRAN program for canonical community ordination by (partial) (detrended) (canonical) correspondence analysis, principal components analysis and redundancy analysis (version 2.1).Technical Rep. LWA-88-02. GLW, Wageningen, 95 pp. (1988).76.Ter Braak, C. J. F. & Smilauer, P. CANOCO 4.5. Biometris. Wageningen University and Research Center, Wageningen, 500 pp. (2002).77.Agnihotri, R. et al. Radiocarbon measurements using new automated graphite preparation laboratory coupled with stable isotope mass-spectrometry at Birbal Sahni Institute of Palaeosciences, Lucknow (India). J. Environ. Radioact. 213, 106156 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

NEWS AND VIEWS
15 September 2021

A spotlight on seafood for global human nutrition

What role might seafood have in boosting human health in diets of the future? A modelling study that assesses how a rise in seafood intake by 2030 might affect human populations worldwide offers a way to begin to answer this.

Lotte Lauritzen

 ORCID: http://orcid.org/0000-0001-7184-5949

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Lotte Lauritzen

Lotte Lauritzen is in the Department of Nutrition, Exercise and Sports, University of Copenhagen, 1958 Frederiksberg C, Denmark.

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An adequate and sustainable supply and intake of nutritious food is essential to tackle major global health issues such as dietary deficiencies. Seafood, which in this context includes fish, shellfish and marine mammals, is rich in micronutrients (such as vitamin A, iron, vitamin B12 and calcium) needed to combat the most common such deficiencies. Seafood is also the dominant source of marine omega-3 fatty acids, which have many health-promoting effects. Writing in Nature, Golden et al.1 present ambitious research that puts seafood centre stage.

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doi: https://doi.org/10.1038/d41586-021-02436-3

References1.Golden, C. et al. Nature https://doi.org/10.1038/s41586-021-03917-1 (2021).Article 

Google Scholar 
2.Food and Agriculture Organization of the United Nations. The State of World Fisheries and Aquaculture 2020. Sustainability in Action (FAO, 2020).3.FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2021. Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All (FAO, 2021).4.Kumssa, D. B. et al. Sci. Rep. 5, 10974 (2015).PubMed 
Article 

Google Scholar 
5.Mithal, A. et al. Osteoporosis Int. 20, 1807–1820 (2009).Article 

Google Scholar 
6.Vuholm, S. et al. Eur. J. Nutr. 59, 1205–1218 (2020).PubMed 
Article 

Google Scholar 
7.Gebauer, S. K., Psota, T. L., Harris, W. S. & Kris-Etherton, P. M. Am. J. Clin. Nutr. 83, 1526S–1535S (2006).PubMed 
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8.Djuricic, I. & Calder, P. C. Nutrients 13, 2421 (2021).PubMed 
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We first identify the location of organic crop fields in Kern County and then estimate whether status as organic versus conventional fields determines pesticide use (Fig. 5).Fig. 5: Methodology overview.Figure outlines the main method steps from identifying organic fields to creating the analysis data to performing the statistical analyses. All images shown are simplified, visual representations of the datasets. CDFA refers to the California Department of Food and Agriculture, while APN is the Assessor’s Parcel Number and TRS is the Township-Range-Section. Identifying organic fields combines the created CDFA organic APN, CDFA organic TRS, and organic pesticides data layers together to create the final organic versus conventional fields layer used in the analysis data section. All analysis data layers are then inputted into the various statistical analyses.Full size imageIdentifying organic fieldsWe identified organic fields using a combination of California Department of Food and Agriculture (CDFA) records and Kern County Agricultural Commissioner’s Office spatial data (“fields shapefiles”) and pesticide use records. No single source was complete, and as such, we evaluated several different approaches to identifying organic fields.California Department of Food and Agriculture (CDFA) recordsData on the location of organic fields, per the California State Organic Program, for 2013–2019 was obtained by request from the California Department of Food and Agriculture (CDFA). The CDFA, through the State Organic Program, requires annual registration of certified organic producers who have an expected gross sale of over $5000. We were specifically interested in the pesticide aspects of organic production, which is governed in our study region by the USDA’s National List of Allowed and Prohibited Substances. The National List of Allowed and Prohibited Substances delineates which synthetic substances can be used and which natural substances cannot be used for pest control in US organic production. Besides substances specifically (dis)allowed on the National List, allowed substances include non-synthetic biological, botanical, and mineral inputs. Field location data were in the form of either Assessor’s Parcel Number (APN) or PLS System Township-Range-Section (TRS) values, though data were reported without systematic formatting. We harmonized the CDFA APN values to merge with the Kern County Assessor’s parcel shapefile (2017), which we then spatially joined with the Kern fields shapefiles. We followed a similar process with PLSS TRS values, which were then merged with the Kern County PLS Section shapefile, and joined to Kern field shapefiles. We refer to our final organic designation as “CDFA Organic”. Details of the data cleaning process are described in the Ancillary Data Processing Methods section below.Using pesticide use reports to refine organic field identificationAfter spot-checking pesticide use on CDFA Organic fields, it became clear we had not entirely eliminated conventional fields. This was likely due to variation in polygon geometries between PLSS Sections, Kern County Assessor parcels, and Kern agricultural fields data. To further refine our classification, we used field-level pesticide use, again from the Kern County Agricultural Commissioner’s Office. As thousands of pesticide products (active ingredients + adjuvants) are in use in Kern County, we took an iterative approach to eliminate fields using conventional pesticides. We first limited the universe of pesticides to those applied to fields that were CDFA Organic. We then identified the 50 most commonly used pesticide products by a number of applications, and manually classified each as organic or conventional. Having identified these products as described below, we matched them back in, eliminating fields that used conventional products and identifying as “PUR Organic” those that used only organic products. We repeated this process, hand identifying the most commonly used products and eliminating fields using conventional products until we had isolated fields using only organic products.To classify a product as organic or conventional, we first searched for each product’s U.S. EPA-registered product label, using the exact product name and EPA product registration number. If there was any indication on the label that the product was certified as organic by the Organic Materials Review Institute (OMRI), or said “for use in organic production” or “organic”, then the pesticide was identified as organic (n = 132). If there was no organic indication on the product label, we searched the OMRI certification database for products with identical names and manufacturers, and identified products as organic if such certifications existed (n = 39). If all ingredients were defined (i.e., no inert or undefined ingredients) and were known organic active ingredients, then the pesticide was identified as organic (n = 1) (Supplementary Data 1). We failed to find EPA-registered labels for three products and confirmed on the California Department of Pesticide Regulation website that they are either inactive or out of production (EPA registration numbers: 52467-50008-AA-5905, 36208-50020-AA, 2935-48-AA-120). Each of the three was rarely used (n  0) to be the same as the mechanisms determining the amount sprayed when some pesticides are used (pesticides when pesticides  > 0). Double-hurdle models64 are an alternative to the Tobit model that allows for the separation of these two decisions.The mechanisms underlying the two decisions (to spray, how much to spray if spraying) can differ such that different covariates can describe each process, and the same covariates are allowed to influence the two processes in different ways (i.e., sign and magnitude can differ). The first, binary decision is usually modeled with a probit model.$${{{{{rm{P}}}}}}left(y=0|{{{{{bf{x}}}}}}right)=1-Phi left({{{{{bf{x}}}}}}gammaright)$$
(1)
Then, the second decision is modeled as a linear model with pesticide use following a lognormal distribution, conditional on positive pesticide use64$$log (y)|{{{{{bf{x}}}}}},y , > , 0 sim {{{{{rm{Normal}}}}}}({{{{{bf{x}}}}}}{{{{{mathbf{upbeta }}}}}},{sigma }^{2})$$
(2)
where Φ is the standard normal cdf, x is a vector of explanatory variables including organic status, y is pesticide use, and ({{{{{mathbf{upbeta }}}}}}) is a vector of coefficients. We use a lognormal hurdle model rather than a truncated normal hurdle model since pesticide use is highly non-normal, and Q-Q plots suggested substantial model improvement using a lognormal rather than normal distribution. In contrast to the panel data models described in the Ancillary Statistical Methods below, our estimation equation used natural log-transformed variables for pesticides (and field and farm size) rather than inverse hyperbolic sine (IHS) transformation since only positive observations are included in the second hurdle model. Following insights derived from our panel data models (Supplementary Notes), we build on the basic hurdle model concept using the farm-by-crop family interaction as a random intercept in both the first and second hurdle. We chose the farm-by-crop family interaction rather than a crossed random effect due to computational feasibility with thousands of permits and hundreds of crops, due to similarity of results to the within estimator model (i.e., fixed effects in causal inference terminology; Supplementary Notes, Supplementary Table 2), and due to AIC/BIC (Supplementary Table 3). Further, we find evidence of heteroskedasticity from both visual inspection and Levine’s test, which adds additional complications to computing crossed random effects. Thus, we proceed with the farm-by-crop family interaction in a random intercept model with cluster robust standard errors clustered at the same grouping. In doing so, observations, where the taxonomic family of the crop was unclear, were dropped. Of the 7367 fields that were dropped due to missing crop families, 6684 were uncultivated agriculture.Our data are effectively repeated cross-sections rather than a true panel since fields are defined by the farm-site-year combination and thus generally change year-to-year or when crops rotate. We model it as such. This implies we do not require observations to have no spray in all time periods, as would be the case in a double hurdle panel model. Linking field IDs over time through spatial processing is complicated by crop rotations of different size areas. Since farmers may farm multiple fields under different management systems, as we illustrate here, and have different contractual obligations at a sub-farm level, requiring farms to never use pesticides on all fields is not reflective of on-the-ground decisions.We repeated the lognormal hurdle models individually for carrots, grapes, oranges, potatoes, and onions, which were the only widely-grown crops with more than 100 organic fields. This allowed for a different slope and intercept by crop type.We conduct several robustness checks. First, we do not have data on crop yields. However, to assess the potential implications of a yield gap on our results, we modify our per hectare rates following Ponisio et al.15 as a robustness check. We group commodities into cereals, roots and tubers, oilseeds, legumes/pulses, fruits, and vegetables and assign them the Ponisio et al.15 yield gap estimates for that group. Crops that did not fall into any of the above groups (e.g., cannabis) were provided the all-crop average from Ponisio et al.15. Second, we analyze how conventional and organic differ with respect to soil quality using a within estimator approach to account for crop-specific differences in soil quality. Third, binary toxicity metrics, while valuable given the number of chemicals and endpoints of interest here, nevertheless fail to distinguish gradations of toxicity for chemicals above (or below) the regulatory threshold. As mentioned above, the data needed to calculate many aggregate indices (e.g., Pesticide Load57 and Environmental Impact Quotient58) are not readily available for all of the chemicals in our study. For completeness, we attempted to calculate the Pesticide Toxicity Index for one well-studied endpoint, fish. We supplemented data provided in Nowell et al.41 with data from Standartox42. However, only about 70% of the chemicals used in our study matched, and pesticide products used on organic fields were more likely to lack toxicity information for one or more chemicals. We briefly discuss the highly preliminary investigation, given the non-random missing toxicity data.All spatial analyses were performed in R Statistical Software v 3.5.3 and all statistical analyses were performed Stata 16 MP. For all tests, statistical significance was based on two-tailed tests with (alpha =0.05.)Ancillary data processing methodsCleaning parcel dataTo spatially locate organic fields, we needed to match the Assessor’s parcel numbers (APNs) provided in the CDFA tabular data to APNs in the Kern County Parcel shapefile (from 2017). Over 90% of the APN entries in the CDFA data were in the format [xxx-xxx-xx], though multiple APNs were often provided in the same cell separated by line breaks, semi-colons, commas, and/or spaces. We made initial edits separating values into individual cells in Microsoft Excel since formatting was highly inconsistent. Observations whose APNs were not in the [xxx-xxx-xx] were modified so that their format matched. In the R environment, dashes were inserted after the third, sixth, and eighth characters (1234567895 became 123-456-78-95) for APNs that did not already contain them. Occasionally, APN numbers were provided with dashes, but with segments of incorrect length (e.g., 12-34-567). In these instances, APN segments were either trimmed from the right or padded with a zero on the left so they matched the [xxx-xxx-xx] format. This approach yielded the greatest number of matches and was checked for accuracy as described below. Additional segments (from APNs with more than two dashes and eight numeric characters) were dropped. A handful of APNs with fewer than eight numeric characters and no dashes were dropped entirely.The edited CDFA APNs were then joined with the Kern County Assessor’s parcel shapefile, creating the “CDFA organic shapefile”. In total, 1637 of 1829 individual CDFA records joined successfully. To evaluate the accuracy of joins between CDFA tabular data, Kern County parcel, and Kern County agricultural spatial data, we spot-checked ownership information using “Company” (CDFA) and “PERMITTEE” (Kern County agricultural data) values.To then identify the crop fields within the organic parcels, we performed a spatial join between the CDFA organic shapefile and the Kern County fields shapefiles. Prior to performing the join, the CDFA parcels’ dimensions were reduced with a 50-m buffer to eliminate spatial joins between CDFA parcels and crop fields that were only touching the parcel margins. Of five different buffer widths evaluated, 50 m reduced the number of false positives and negatives, as determined by comparing the “Company” and “PERMITTEE” values. We refer to the fields that match as “APN Organic”.Cleaning PLSS Township-Range-Section valuesEach year several producers reported Township, Section, and Range (TRS) values, consistent with the PLS System (PLSS), rather than APN values. We used these TRS values to identify PLSS Sections that contained organic fields.We separated any cell containing multiple TRS values and removed any prefixes such as “S”, “Section”, “Sec.”, “T”, and “R” that would prevent joining to Kern County PLSS spatial data in Excel. In the R environment, we padded the left side of the “S” value with a 0 if it was a single digit, then concatenated the three columns into a “TRS” column. We joined TRS from the CDFA tabular data to PLSS spatial data, which identified 563 Sections as containing organic fields, from 2013 to 2019, out of a total of 664 unique TRS codes in the CDFA dataset. We then performed a spatial join between PLSS Sections that contain organic fields and Kern County fields shapefiles, to identify all agriculture fields that overlap with those Sections. Additional processing using the Pesticide Use Reports is described above.Ancillary statistical methodsWe began with a pooled ordinary least squares (OLS) model that, as the name suggests, pools observations over farms, years, and crop types. However, there may be attributes of crops or farms that may be systematically different between organic and conventional, and this systematic difference could bias our pooled OLS results. To address this, we first considered propensity score approaches but were unable to find a sufficient balance of our covariate distribution between organic and conventional fields. As an alternative, we limited our sample to fields with overlapping farmers and crop types. In other words, we focused on the subset of fields that are grown by farmers producing both organic and conventional fields and to crops that are produced both conventionally and organically. However, this shrunk our dataset by two-thirds.To leverage more of our data, we next considered panel data models as a means to address unobserved variables. We consider both within-estimator models (also known as “fixed effects” in causal inference terminology, but different from the biostatistical use of the term) and random effects models (with random intercepts), seeking to capture characteristics of the crop, grower, and year. The advantage of a within-estimator approach is that the omitted variables are removed (through differencing) and thus, they can be correlated with covariates without biasing the estimation. In other words, pesticide use and all covariates are differenced from their crop-specific mean (or crop family, farmer, etc. specific mean, depending on the model). In doing so, the propensity for certain crops (crop family, farmer) to be grown organic or to be fast or slow adopters of new technologies is removed. The disadvantage is that characteristics shared by all fields of a crop (e.g., value) are lost in the differencing, and more importantly, that the differencing is not easily translated to nonlinear models that we employ later in the analysis. Random effects are more easily translated to nonlinear models. The disadvantage of random effects is the strong assumption that the unobserved variables are uncorrelated with the covariates18,65, which is required for random effects coefficient estimates to be unbiased. Here, we see the difference in coefficient estimates between the within-estimator and random effects models are quite small (Supplementary Table 2).Random effects particularly crossed random effects with thousands of permits and hundreds of crops, introduce computational challenges due to large, sparse matrices. Further, we find evidence of heteroskedasticity from both visual inspection and Levine’s test, which adds additional complications to computing crossed random effects. We proceed using the farm-by-crop family interaction in a random intercept model with cluster robust standard errors clustered at the same grouping based on AIC/BIC (Supplementary Table 3), computational feasibility, and similarity to the within-estimator results (Supplementary Table 2). Observations, where the taxonomic family of the crop was unclear, were dropped in any models including family in either the random effects or the cluster robust standard errors. Of the 7367 fields that were dropped due to missing crop families, 6684 were uncultivated agriculture.In the panel data models, we used IHS transformations to accommodate highly non-normal pesticide (and field and farm size) data. IHS is very similar to natural log transformation66 but is defined at zero, which is important given a sizable fraction of our observations have zero pesticide use. As with log–log transformations, IHS–IHS transformation can be interpreted as elasticities. We pre-multiply pesticide use by 100 to improve estimation66, though this does not affect interpretation. As described above, we leverage insights on model specification from the panel data models, but rely on the double hurdle models to parse apart the decision to spray from the decision of how much to spray.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

The absence of the first principles for biological systems in general, and in particular for vegetation populations where phenomena are interconnected makes their mathematical modelling complex. The theory of vegetation pattern formation rests on the self-organisation hypothesis and symmetry-breaking instability that provoke the fragmentation of the uniform cover. The symmetry-breaking instability takes place even if the environment is isotropic31,33,35. This instability may be an advection-induced transition that requires the pre-existence of the environment anisotropy due to the topography of the landscape34,39,40. Generally speaking, this transition requires at least two feedback mechanisms having a short-range activation and a long-range inhibition. In this respect, we consider three different vegetation models that are experimentally relevant systems: (i) the generic interaction redistribution model describing vegetation pattern formation which incorporates explicitly the facilitation, competition and seed dispersion nonlocal interactions (ii) the local nonvariational partial differential model described by a nonvariational Swift–Hohenberg type of model equation, and (iii) the reaction–diffusion system that incorporate explicetely water transport.The interaction-redistribution approachThe integrodifferential modelThis approach consists of considering a well-known logistic equation with nonlocal plant-to-plant interactions. Three types of interactions are considered: the facilitative (M_{f}(mathbf {r},t)), the competitive (M_{c}(mathbf {r},t)), and the seed dispersion (M_{d}(mathbf {r},t)) nonlocal interactions. To simplify further the mathematical modelling, we consider that the seed dispersion obeys a diffusive process (M_{d}(mathbf {r},t)approx nabla ^{2}b(mathbf {r},t)), with D the diffusion coefficient, b the biomass density, and (nabla ^{2}=partial ^2/partial x^2+partial ^2/partial y^2) is the Laplace operator acting in the (x,y) plane. The interaction-redistribution reads$$begin{aligned} M_{i}=expleft{ frac{xi _{i}}{N_{i}}int b(mathbf {r}+mathbf {r}’,t)phi _i(r,t)dmathbf {r}’right} , { text{ with } } phi _i(r,t)= exp(-r/L_{i}) end{aligned}$$
(1)
where (i=f,c). (xi _i) represents the strength of the interaction, (N_i) is a normalisation constant. We assume that their Kernels (phi _i(r,t)) are exponential functions with (L_i) the range of their interactions. The facilitative interaction (M_{f}(mathbf {r},t)) favouring vegetation development. They involve the accumulation of nutrients in the neighbourhood of plants, the reciprocal sheltering of neighbouring plants against climatic harshness which improves the water budget in the soil. The range of the facilitative interaction (L_f) operates on the crown size. The competitive interaction operates over a length (L_c) and involves the below-ground structures, i.e., the rhizosphere. In nutrient-poor or/and in water-limited territories, lateral spreading may extend beyond the radius of the crown. This extension of roots relative to their crown size is necessary for the survival and the development of the plant in order to extract enough nutrients and/or water from the soil. When incorporating these nonlocal interactions in the paradigmatic logistic equation, the spatiotemporal evolution of the normalised biomass density (b(mathbf {r}, t)) in isotropic environmental conditions reads14$$begin{aligned} partial _{t} b(mathbf {r},t)=b(mathbf {r},t)[1-b(mathbf {r},t)]M_{f}(mathbf {r},t)- mu b(mathbf {r},t)M_{c}(mathbf {r},t)+Dnabla ^{2}b(mathbf {r},t). end{aligned}$$
(2)
The normalisation is performed with respect to the total amount of biomass supported by the system. The first two terms in the logistic equation with nonlocal interaction Eq. (2) describe the biomass gains and losses, respectively. The third term models seed dispersion. The aridity parameter (mu) accounts for the biomass loss and gain ratio, which depends on water availability and nutrients soil distribution, topography, etc. The homogeneous cover solutions of Eq. (2) are: (b_{o}=0) which corresponds to the state totally devoid of vegetation, and the homogeneous cover solutions satisfy the equation$$begin{aligned} mu =(1-b)exp (Delta b), end{aligned}$$
(3)
with (Delta =xi _{f}-xi _{c}) measures the community cooperativity if (Delta >0) or anti-cooperativity when (Delta 0). The solution (u_{-}) is always unstable even in the presence of small spatial fluctuations. The linear stability analysis of vegetated cover ((u_{+})) with respect to small spatial fluctuations, yields the dispersion relation$$begin{aligned} sigma (k)=u_{+}(kappa -2u_{+})-(nu -gamma u_{+})k^{2}-alpha u_{+}k^{4}. end{aligned}$$
(8)
Imposing (partial sigma /partial k|_{k_{c}}=0) and (sigma (k_{c})=0), the critical mode can be determined$$begin{aligned} k_{c}=sqrt{frac{gamma -nu /u_{c}}{2alpha }}, end{aligned}$$
(9)
where (u_{c}) satisfies (4alpha u_{c}^2(2u_{c}-kappa )=(2gamma u_{c}-nu )^2). The corresponding aridity parameter (eta _{c}) can be calculated from Eq. (7).The reaction–diffusion approachThe second approach explicitly adds the water transport by below ground diffusion. The coupling between the water dynamics and the plant biomass involves positive feedbacks that tend to enhance water availability. Negative feedbacks allow for an increase in water consumption caused by vegetation growth, which inhibits further biomass growth.The modelling considers the coupled evolution of biomass density (b(mathbf {r},t)) and groundwater density (w(mathbf {r},t)). In its dimensionless form, this model reads33$$begin{aligned} frac{partial b}{partial t}= & {} frac{gamma w}{1+omega w}b-b^{2}-theta b+nabla ^{2}b, end{aligned}$$
(10)
$$begin{aligned} frac{partial w}{partial t}= & {} p-(1-rho b)w-w^{2}b+delta nabla ^{2}(w-beta b). end{aligned}$$
(11)
The first term in the first equation describes plant growth at a constant rate ((gamma /omega)) that grows linearly with w for dry soil. The quadratic nonlinearity (-b^{2}) accounts for saturation imposed by poor nutrients soil. The term proportional to (theta) accounts for mortality, grazing or herbivores. The mechanisms of dispersion are modelled by a simple diffusion process. The groundwater evolves due to a precipitation input p. The term ((1-rho b)w) in the second equation accounts for the evaporation and drainage, that decreases with the presence of vegetation. The term (w^{2}b) models the water uptake by the plants due to the transpiration process. The groundwater movement follows the Darcy’s law in unsaturated conditions; that is, the water flux is proportional to the gradient of the water matric potential41. The matric potential is equal to w, under the assumption that the hydraulic diffusivity is constant41. To model the suction of water by the roots, a correction to the matric potential is included; (-beta b), where (beta) is the strength of the suction. More